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地球信息科学学报 >

2018 , Vol. 20 >Issue 6: 862 - 870

DOI: https://doi.org/10.12082/dqxxkx.2018.180100

2017年中国地理信息科学理论与方法学术年会优秀论文专辑

长江三角洲城市化地区植被初级生产力的时空变化研究

  • 裴凤松 , 1 ,
  • 王坤 1 ,
  • 刘小平 , 2, 1, * ,
  • 吴长江 1 ,
  • 周义 1 ,
  • 刘利安 1
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  • 1. 江苏师范大学地理测绘与城乡规划学院,徐州 221116
  • 2. 中山大学地理科学与规划学院,广州 510275
*通讯作者：刘小平（1978-）,男,教授,研究方向为地理模拟、空间智能及优化决策等。E-mail:

作者简介：裴凤松（1982-）,男,博士,副教授,研究方向为GIS和地理模拟。E-mail:

收稿日期: 2018-02-08

  要求修回日期: 2018-03-28

  网络出版日期: 2018-06-20

基金资助

国家自然科学基金项目(41401438)

大学生创新训练计划项目（201710320030）

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Temporal-spatial Dynamics of Vegetation Primary Productivity in the Urbanized Areas in the Yangtze River Delta, China

  • PEI Fengsong , 1 ,
  • WANG Kun 1 ,
  • LIU Xiaoping , 1, 2, * ,
  • WU Changjiang 1 ,
  • ZHOU Yi 1 ,
  • LIU Li'an 1
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  • 1. School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
  • 2. School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
*Corresponding author: LIU Xiaoping, E-mail:

Received date: 2018-02-08

  Request revised date: 2018-03-28

  Online published: 2018-06-20

Supported by

National Natural Science Foundation of China, No.41401438

National Students' Project for Innovation Traing Program, No.201710320030

Copyright

《地球信息科学学报》编辑部 所有
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摘要

城市化过程对植被初级生产具有重要影响。以往研究主要集中于城市用地扩张对植被初级生产力的直接影响分析,而较少关注其间接效果。本文以长江三角洲地区为例,分别从地区尺度和城市尺度分别分析了2000-2013年植被初级生产力的时空变化,探讨了其与气温、降水量及城市建成区绿化覆盖率的关系。研究表明：地区尺度上,2000-2013年长江三角洲植被初级生产力呈现不断增加,其中城市建成区植被初级生产力呈现显著增加的趋势（P<0.05）;城市尺度上,城市建成区内植被初级生产力主要呈现增加的趋势,而其外围缓冲区内则与此相反。在当前气候变化背景下,这可能与城市建成区绿化覆盖率不断增加,及快速的城市扩张有关。

关键词： 城市建成区; 植被初级生产力; 绿化覆盖率; 归因分析; 长江三角洲

本文引用格式

裴凤松 , 王坤 , 刘小平 , 吴长江 , 周义 , 刘利安 . 长江三角洲城市化地区植被初级生产力的时空变化研究[J]. 地球信息科学学报, 2018 , 20(6) : 862 -870 . DOI: 10.12082/dqxxkx.2018.180100

Abstract

The process of urbanization, especially the urban land expansion, frequently shows an important influence on vegetation primary productivity. Past studies mainly focused on the direct impacts of urban land sprawl on the vegetation primary productivity, such as transformation of natural land use into urban impervious surface. However, little effort was exerted to understand the indirect impacts of urban land use (i.e., changes of urban greenery coverage). Taking the Yangtze River Delta, China as a case study, this paper analyzed the temporal and spatial changes of vegetation primary productivity in the study area during 2000-2013. The analysis was conducted by cities and over the whole region, respectively. The relationships between vegetation primary productivity in the urban built-up area and the corresponding size of the built-up area were further analyzed for cities by using statistical analysis. Mechanisms of the changes in vegetation primary productivity were explored from both the large and local scales. That is, correlations were examined between vegetation primary productivity and regional meteorological factors (i.e., the annual mean temperature and total precipitation), as well as greenery coverage rate over the built-up area at different cities. The results show that the vegetation primary productivity in the study area showed an overall increased trend from 2000 to 2013 at a regional scale. In particular, the average vegetation primary productivity revealed a significant increasing trend during 2000-2013 in the urban built-up areas in the Yangtze River Delta （P < 0.05）. At city scale, the vegetation primary productivity mainly revealed increased trends over the period 2000-2013 in the built-up area in most of the cities. However, the trends were reversed in the 0~10 km buffer zone of the built-up areas. Under the condition of climate change, this increase of vegetation primary productivity might be associated with the increase of urban greenery coverage in the urban built-up areas, as well as the fast urban expansion in the Yangtze River Delta.

Key words： build-up area; primary productivity; greenery coverage; attribution analysis; Yangtze River Delta

1 引言

城市地区是人类活动对自然环境作用最剧烈的区域。过去二三十年来,全球城市化进程呈现加速发展趋势[1]。城市化过程常常导致土地利用/覆被剧烈变化,城市建成区也随之不断增加。城市扩张过程对植被初级生产力变化产生重要影响,对生态系统服务功能也具有重要作用[2]。植被初级生产力反映了自然条件下植被的生产能力,是表征生态系统碳源/汇的关键因素之一[3]。国内外学者已经开展了城市扩张对植被初级生产力影响相关研究。例如,Zhao等[4]以美国密歇根州东南部10个城市为例研究发现城市用地的低密度开发增加了植被的总初级生产力。Liu等[5]研究发现2000-2013年武汉市大量农田转化为城市建成区,并导致了巨大的植被总初级生产力损失。Imhoff等[6]和Pei等[7]发现城市用地扩张可能增加/降低植被净初级生产力。Wu等[8]分析了1999-2010年长江三角洲的城市扩张对植被NPP的影响,他们指出研究区的城市扩张降低了植被NPP的分布。因而,城市土地利用/覆被变化对植被初级生产力变化的影响具有较大的不确定性。另外,以往研究多是从城市用地扩张的直接影响方面（主要是城市不透水面取代自然地表）来探讨城市化对植被生产力的影响,而在其间接影响（如城市建成区植被绿化）方面的认识则不足[9]。
长江三角洲地区是中国城市化水平最高的区域之一。针对快速城市化背景下长江三角洲城市扩张的植被初级生产力时空响应不确定性问题,本文基于2000-2013年时间序列卫星植被指数数据和DMSP/OLS夜间灯光数据,以长江三角洲为例,分析了城市化地区,尤其是城市建成区植被初级生产力的动态变化趋势;同时,分析其与年平均气温、年降水等自然因素,以及城市建成区绿化覆盖率等人类活动因素的相关关系,为城市可持续管理及调控提供理论依据。

2 研究区概况、数据源及研究方法

2.1 研究区概况

长江三角洲地区位于118°~123° E,28°~34° N,总面积约10万km2。北部地势低平,而南部则丘陵广布,主要包括上海市、江苏省南部和浙江省北部。具体来说,包括上海市、江苏省的8个地级市（扬州、泰州、南通、南京、镇江、常州、无锡和苏州）和浙江省的7个地级市（湖州、嘉兴、杭州、绍兴、宁波、舟山和台州）（图1）,形成了我国密度最大的城市带,构成长江三角洲城市群的中心城市体系,是长江三角洲城市群的主体。长江三角洲地区处于亚热带季风气候分布区,年平均气温16 ℃,年降水量约1158 mm。近年来,长江三角洲地区各城市的社会经济发展水平快速发展,GDP由2000年的16 028亿元增长到2013年的97 770亿元。在此过程中,城市化水平也快速提升,城市建成区面积由2000年的1703 km2增加到2013年的4394 km2[10],从而导致大量的农田损失[11,12]。城市建成区绿化覆盖率也呈现不断增加的趋势,如2000-2013年上海市的建成区绿化覆盖率增长了84%。长江三角洲的快速城市化过程对植被初级生产产生重要影响。
Fig. 1 Location of study area

图1 研究区位置

2.2 数据来源及处理

本文收集的数据主要包括：2000-2013年长江三角洲各城市气象站观测的年平均气温、降水数据、2000-2013年的DMSP-OLS夜间灯光数据、归一化植被指数（NDVI）数据和其他社会经济相关数据等（表1）。其中,气温数据和降水数据来自中国气象局气象数据中心并经过严格的质量控制。在地理信息系统（GIS）支持下,通过使用反距离加权（IDW）插值方法将站点观测数据处理得到空间化的数据。夜间灯光数据是从地理国情监测云平台（http://www.dsac.cn）下载的DMSP-OLS稳定夜间灯光数据。基于卫星的植被NDVI数据来源于MODIS数据产品（MOD13A3）,从美国地质勘探局（USGS）的陆面过程分布式存档中心(http://lpdaac.usgs.gov)下载获取,数据分辨率为1 km。考虑到MODIS数据产品正弦投影的特点,使用MODIS重投影工具（MRT）来进行影像重投影,再在GIS的支持下进行裁剪处理、统计分析。此外,本文收集了森林类型的样地干物质和净初级生产力数据[13]。社会经济相关数据主要包括城市建成区面积、建成区绿化覆盖率等,数据来源于2000-2013年中国城市统计年鉴[10]。
Tab. 1 Data sources in this study

表1 本文所用数据及来源

数据类型 分辨率 数据来源
气象数据 站点 中国气象局气象
数据中心
DMSP-OLS夜间灯光数据 1 km 地理国情监测云平台
NDVI数据 1 km、 http://lpdaac.usgs.gov
植被生产数据 森林样地 罗天祥[13]
社会经济数据 - 中国城市统计年鉴

2.3 研究方法

2.3.1 植被初级生产力估计
植被初级生产力建模常用的有统计模型、参数模型和过程模型[14]。统计模型估算的结果往往难以反映自然干扰、人为扰动的影响,主要模拟的是潜在植被初级生产力。过程模型较为复杂,涉及的参数多,往往较难获取[15]。参数模型中光能利用率参数大小与植被类型、空间尺度及冠层结构等密切相关,具有高度的复杂性和不确定性,尤其是对于人工生态系统的城市化地区[5]。以往研究发现,基于时间集成的植被NDVI与植被初级生产力存在线性相关关系,常常被用来反映植被初级生产力的大小[16,17],被广泛应用于评估样地水平 [18]、国家层面[19]和全球尺度的植被生产[20]。考虑其易于计算及有效性,本文基于此方法估算长江三角洲地区植被初级生产力（包括城市建成区植被初级生产力）。具体地,植被初级生产力（ P Nd ）由每个月的NDVI值与天数（ days ）的乘积累加来确定。
P Nd = ∑ m = 1 12 NDVI × days （1）
当月平均温度小于等于0 ℃时,由于低温的约束,绿色植被较少发生光合作用,该月的植被初级生产力被设定为0,具体的模型描述参见Prince[16]和Imhoff等[19]研究。
2.3.2 植被初级生产力变化趋势分析
为了研究14年来植被初级生产力变化状况,及变化速率在空间上的分异,从地区平均植被生产力和逐象元植被初级生产力2个方面分析植被初级生产力的变化趋势。具体来说,假设植被初级生产力变化与年份呈线性关系,则植被初级生产力的变化（ P Nd ）按式（2）计算。
P Nd = a + b ∙ T （2）
式中： T 为时间变量（2000-2013年）; a 和 b 分别为模型的回归系数。当 b 大于（或小于）零,且通过显著性检验时（P<0.05）,则说明植被初级生产力呈现出显著增加（或减少）的趋势。
2.3.3 基于统计数据和夜间灯光数据的城市建成区提取及与植被初级生产力关系分析
作为人类活动的表征,夜间灯光数据往往包含了道路、居民地等与城市化发展密切相关的信息[21]。本文通过结合城市建成区面积统计数据和DMSP/OLS夜间灯光数据来提取城市建成区用地[22]。在此基础上,分析城市建成区内部（0 km）、城市建成区外围5 km范围（0~5 km）和城市建成区外围5~10 km范围内的植被初级生产力动态变化,进而探讨导致长江三角洲地区植被初级生产力变化的可能原因。通常,植被初级生产力变化受到气候变化的影响,同时也受到人类活动的强烈制约。例如,气温、降水等因素在宏观尺度上对植被初级生产力产生影响,而社会、经济等因素往往对局地植被初级生产力具有决定性的影响[8]。因而,本文首先分析了长江三角洲地区植被初级生产力与大尺度气象要素（平均气温、降水量）之间的关系。另外,探讨了长江三角洲城市建成区植被初级生产力与城市建成区绿化覆盖率之间的相关关系。
如图2所示,本文分析步骤为：① 基于DMSP-OLS夜间灯光影像,根据统计资料中城市建成区面积按城市提取城市建成区和非建成区用地的时空分布;② 计算城市建成区和非建成区的植被初级生产力分布（NDVI>=0.1）[23];③ 利用线性回归分析方法分析14年来植被初级生产力的时空变化趋势;④ 结合14年植被初级生产力的估算结果、气象数据（气温、降水量）和城市化发展因子数据（建成区绿化覆盖率）,进行植被初级生产力的归因分析。
Fig. 2 Flowchart of the changes of vegetation primary productivity and the driving factors in the Yangtze River Delta

图2 长江三角洲地区植被初级生产力变化及归因分析框架

3 结果和讨论

3.1 2000-2013年长江三角洲地区植被初级生产力 变化趋势

3.1.1 植被初级生产力估算精度分析
根据计算,长江三角洲地区平均植被初级生产力从2000年的182.4 NDVI·days增加到2013年的185.6 NDVI·days。依据基于样地数据的空间对比分析,本文估算的2000-2013年平均植被初级生产力结果与6个森林样地[13]的植被净初级生产力变化呈现较好的一致性（R=0.845;N=6;P=0.034）。年际变化方面,本文计算结果与基于模型估算的2000-2013年长江三角洲地区植被净初级生产力时间序列基本吻合[8]（R=0.638;N=14;P=0.014）。因此可认为该方法适于长江三角洲地区植被初级生产力的研究。
3.1.2 植被初级生产力时间变化趋势
本文分别从地区总量和空间分异2个方面来分析植被初级生产力变化趋势。2000-2013年,长江三角洲地区植被初级生产力总体上呈现增加的趋势（0.168 NDVI·days·year-1,R2=0.094,P=0.286）,这与李广宇等[23]的研究结论基本一致。为了进一步定量分析城市建成区植被初级生产力的变化,本文在提取的建成区范围基础上,分别计算获取长江三角洲各城市的城市建成区和非建成区植被初级生产力的变化趋势。由图3所示,不论是城市建成区还是非建成区,长江三角洲地区植被初级生产力均呈现出增加的趋势。其中,非城市建成区的植被初级生产力增加速率为0.145 NDVI·days·year-1（R =0.062,P=0.392）,而城市建成区的植被初级生产力呈现显著增加的趋势,达到0.941 NDVI·days·year-1（R2=0.761,P=0.000）。
Fig. 3 Changes of vegetation primary productivity in urban built-up / non-built-up areas in the Yangtze River Delta

图3 长江三角洲城市建成区/非建成区植被初级生产力变化

3.1.3 植被初级生产力空间变化趋势
除了区域植被初级生产力总量的变化,不同地区植被初级生产力变化呈现明显的空间异质性。如图4（a）所示,约31%的地区植被初级生产力呈减少趋势,减少的区域主要分布在地势较为平坦的长江三角洲中部、北部地区,这与DMSP-OLS夜间灯光数据的高值区域分布基本一致,说明植被初级生产力的减少可能与人类活动的影响有关[6]。另外,植被初级生产力呈现显著增加的面积约占总面积的53%,植被初级生产力呈现显著增加的区域主要分布在长江三角洲南部的山地丘陵和亚热带常绿阔叶林分布的非城市用地地区（图4（b））。需要注意的是,上海、南京、杭州等大城市的城市用地植被初级生产力也呈现出增强趋势（图4（a））。因而,本文进一步分析了长江三角洲城市建成区及其周围地区植被初级生产力的梯度变化。
Fig. 4 Temporal-spatial changes of vegetation primary productivity in the Yangtze River Delta from 2000 to 2013

图4 2000-2013年长江三角洲地区植被初级生产力时空变化

3.2 城市建成区及其向外不同距离缓冲区植被初 级生产力的梯度变化

为了进一步剖析长江三角洲地区植被初级生产力的变化趋势,分析其与社会经济等因素的关系,本文按城市分别分析了城市建成区（0 km）、城市建成区外5 km（0~5 km）和城市建成区外5~10 km范围内植被初级生产力的变化趋势。如表2所示,在城市建成区范围内,除了泰州、台州和舟山市,其他各城市植被初级生产力呈现增加的趋势。其中,常州、南京、南通、苏州、扬州、上海、杭州、湖州和绍兴等城市的植被初级生产力的增加趋势均通过了5%的显著性检验,这可能与城市建成区绿化覆盖率提高等有密切关系。另外,本文分析了城市建成区外5 km范围（0~5 km）的植被初级生产力变化趋势 （表2）,发现随着城市的扩展,大部分城市的植被初级生产力呈现降低的趋势,尤其是泰州、镇江、嘉兴、宁波和台州等城市通过了0.05的显著性检验（P<0.05）;对于建成区外5~10 km范围,植被初级生产力变化与城市建成区范围外5 km范围（0~5 km）的变化规律相似,这可能与城市建成区外围城市用地开发相对较晚,城市建成区绿化发展慢于城市发展水平所致。需要注意的是,上海、南京等城市的建成区植被初级生产力仍然呈现增加的趋势。特别地,上海市的建成区植被初级生产力呈现显著增加的趋势（P<0.05）,这可能与城市的经济发展、城市建成区绿化覆盖率较高等有密切关系。
Tab. 2 Changes in the vegetation primary productivity in the built-up areas and its different buffer areas in the Yangtze River Delta

表2 长江三角洲城市建成区及不同距离缓冲区内植被初级生产力的变化趋势

城市名 0 km 0~5 km 5~10 km
回归系数 显著性 回归系数 显著性 回归系数 显著性
常州 1.615 0.001 -0.667 0.080 -0.614 0.063
南京 1.973 0.000 0.850 0.078 0.899 0.007
南通 1.649 0.003 -0.307 0.500 0.169 0.599
苏州 2.367 0.000 0.032 0.949 -0.802 0.105
泰州 -0.640 0.381 -1.677 0.011 -0.172 0.782
无锡 0.720 0.151 -0.769 0.050 -0.944 0.004
扬州 1.421 0.004 0.393 0.144 0.895 0.009
镇江 0.807 0.063 -0.895 0.018 -0.453 0.380
上海 2.548 0.000 1.511 0.047 0.681 0.282
杭州 0.934 0.017 -0.419 0.100 -0.077 0.851
湖州 0.919 0.037 0.269 0.429 -0.566 0.259
嘉兴 0.440 0.111 -1.491 0.000 -1.202 0.010
宁波 0.668 0.093 -0.812 0.034 0.539 0.209
绍兴 1.302 0.008 0.089 0.755 0.650 0.003
台州 -0.564 0.305 -0.973 0.002 -0.618 0.127
舟山 -1.097 0.066 0.475 0.053 -0.117 0.758

3.3 城市建成区植被初级生产力与城市建成区面 积变化关系

本文进一步分析了2000-2013年长江三角洲地区城市建成区植被初级生产力与城市建成区面积的变化趋势。根据分析,城市建成区面积以101.29 km2 year-1的速率呈现显著的增加（R2=0.984;P=0.000）。另外,根据城市建成区区域年平均植被初级生产力变化与城市建成区面积的时间序列数据分析,长江三角洲地区城市建成区植被初级生产力与建成区面积之间具有显著的正相关关系（R=0.877;N=14;P=0.000）,这表明随着建成区面积的扩大,长江三角洲城市建成区植被初级生产力也呈现不断增加的趋势。本文进一步开展了逐城市的城市建成区植被初级生产力与建成区面积之间的相关关系分析。表3为各个城市建成区用地植被初级生产力与其建成区面积的相关关系及其显著性水平。结论指出,大部分城市的建成区用地植被初级生产力与其建成区面积呈现显著的正相关关系（P<0.05）,而泰州、台州和舟山市的城市建成区用地植被初级生产力与其建成区面积呈现负相关关系,却并不具统计学意义（P>0.05）。
Tab. 3 Relationships between changes of the sizes of urban built-up area and vegetation primary productivity

表3 城市建成区面积与植被初级生产力的关系

常州 南京 南通 苏州 泰州 无锡 扬州 镇江
回归系数 0.784 0.979 0.733 0.880 -0.239 0.500 0.741 0.529
显著性 0.001 0.000 0.003 0.000 0.410 0.069 0.002 0.052
上海 杭州 湖州 嘉兴 宁波 绍兴 台州 舟山
回归系数 0.727 0.641 0.706 0.469 0.566 0.553 -0.354 -0.118
显著性 0.003 0.013 0.005 0.091 0.035 0.050 0.214 0.687

3.4 长江三角洲植被初级生产力变化归因分析

3.4.1 植被初级生产力与气象驱动因子关系
图5、6反映了2000-2013年长江三角洲地区植被初级生产力和年平均气温、年降水量的变化趋势。如图5所示,长江三角洲地区年平均气温在2007年之前呈现明显的增温趋势,此后开始降低;而年平均降水量主要呈现波动上升的趋势（图6）。根据计算,2000-2013年长江三角洲地区植被初级生产力与年平均气温呈现较为一致的变化（R=0.311;N=14;P=0.279）。相似地,其与降水量变化也具有相似的相关关系（R=0.282;N=14;P=0.328）。因而,在当前气候变化背景下,气温、降水等气象要素可能对植被初级生产力变化具有重要影响。除了宏观层面上的分析,本文进一步分析了城市建成区植被初级生产力与城市建成区绿化覆盖率的变化关系。
Fig. 5 Changes of vegetation primary productivity and annual mean temperature in the Yangtze River Delta from 2000 to 2013

图5 长江三角洲地区2000-2013年植被初级生产力和年平均气温的变化

Fig. 6 Changes of vegetation primary productivity and annual total precipitation in the Yangtze River Delta from 2000 to 2013

图6 长江三角洲地区2000-2013年植被初级生产力和年降水量的变化

3.4.2 城市建成区植被初级生产力与建成区绿化覆盖率的变化关系
14年来,长江三角洲地区建成区绿化覆盖率不断提高,各城市的年建成区绿化覆盖率呈现不同程度的增加。城市绿地对城市植被初级生产力具有重要影响[24]。本文分别从地区尺度和城市尺度分析城市建成区植被初级生产力与建成区绿化覆盖率的相互关系。根据计算,长江三角洲地区城市建成区植被初级生产力与建成区平均绿化覆盖率呈现显著的正相关关系（R=0.808;N=14;P=0.000）。另外,该相关关系在不同等级城市之间具有明显的差异性（表4）。具体来说,泰州、绍兴、台州和舟山市呈现负相关的特点,然而,该相关关系并不显著。对于大部分城市来说,其城市建成区绿化覆盖率与建成区内植被初级生产力呈现正相关关系。特别地,常州、南京、苏州、扬州、镇江和杭州等城市的相关性水平通过了5%的显著性检验,这说明城市建成区植被初级生产力的增加可能与城市建成区绿化覆盖率的不断提高有关。
Tab. 4 Relationships between greenery coverage changes in the urban built-up area and vegetation primary productivity

表4 城市建成区绿化覆盖率与植被初级生产力的关系

常州 南京 南通 苏州 泰州 无锡 扬州 镇江
相关系数 0.686 0.730 0.422 0.733 -0.227 0.103 0.619 0.575
显著性 0.007 0.003 0.133 0.003 0.436 0.727 0.018 0.032
上海 杭州 湖州 嘉兴 宁波 绍兴 台州 舟山
相关系数 0.492 0.592 0.529 0.453 0.379 -0.044 -0.332 -0.180
显著性 0.074 0.026 0.052 0.104 0.182 0.881 0.246 0.538

4 结论

针对当前城市化地区,尤其是城市建成区植被初级生产力变化及其成因的不确定性问题,本文以长江三角洲为例,分析了2000-2013年植被初级生产力的时空变化,探讨了气温、降水等自然条件及建成区绿化覆盖率变化等与人类活动相关因子对植被初级生产力的影响。研究发现,对于长江三角洲地区大部分城市,城市建成区范围植被初级生产力主要呈现增加的趋势,而其向外围0~10 km缓冲区则与此相反。在当前气候变化背景下,城市建成区植被初级生产力的变化可能与城市建成区绿化覆盖率不断增加,及快速的城市扩张有关。然而,类似现象在其它地区城市是否也存在,其与城市热岛效应有何关系?这些问题仍需要作进一步分析。下一步研究将开展多个城市群的深入研究和对比分析,以弄清城市建成区植被初级生产力变化与城市发展水平之间的复杂变化关系,深入探讨其驱动机制,为城市管理调控提供理论支撑。

The authors have declared that no competing interests exist.

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朴世龙,方精云,郭庆华.利用CASA 模型估算我国植被净第一性生产力[J].植物生态学报,2001,25(5):603-608.

DOI

[ Piao S L, Fang J Y, Guo Q H, et al.Application of CASA model to the estimation of Chinese terrestrial net primary productivity[J]. Acta Phytoecologica Sinica, 2001,25(5):603-608. ]

[16]
Prince S D.A model of regional primary production for use with coarse resolution satellite data[J]. International Journal of Remote Sensing, 1991,12(6):1313-1330.A model of crop primary production, which was originally developed to relate the amount of absorbed photosynthetically active radiation (APAR) to net production in field studies, is discussed in the context of coarse resolution regional remote sensing of primary production. The model depends on an approximately linear relationship between APAR and the normalized difference vegetation index. A more comprehensive form of the conventional model is shown to be necessary when different physiological types of plants or heterogeneous vegetation types occur within the study area. The predicted variable in the new model is total assimilation (net production plus respiration) rather than net production alone or harvest yield.

DOI

[17]
Goward S N,Tucker C J,Dye D G.North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer[J]. Vegetatio, 1985,64(1):3-14.Spectral vegetation index measurements derived from remotely sensed observations show great promise as a means to improve knowledge of land vegetation patterns. The daily, global observations acquired by the Advanced Very High Resolution Radiometer, a sensor on the current series of U.S. National Oceanic and Atmospheric Administration meteorological satellites, may be particularly well suited for global studies of vegetation. Preliminary results from analysis of North American observations, extending from April to November 1982, show that the vegetation index patterns observed correspond to the known seasonality of North American and cultivated vegetation. Integration of the observations over the growing season produced measurements that are related to net primary productivity patterns of the major North American natural vegetation formations. Regions of intense cultivation were observed as anomalous areas in the integrated growing season measurements. These anomalies can be explained by contrasts between cultivation practices and natural vegetation phenology. Major new information on seasonality, annual extent and interannual variability of vegetation photosynthetic activity at continental and global scales can be derived from these satellite observations.

DOI

[18]
Plant R E,Munk D S,Roberts B R, et al.Application of remote sensing to strategic questions in cotton management and research[J]. Journal of Cotton Science, 2001,1:30-41.Remote sensing can be a relatively inexpensive source of data for site-specific crop management. Many potential applications of remote sensing are tactical, in that they involve responses to particular conditions or situations that arise during the course of the season. Other potential applications, however, both in research and in management, involve strategic questions that concern the integrated whole of the crop production system. Strategic decision making generally occurs before the season begins. The use of remotely sensed images in addressing strategic vs. tactical questions differs in that strategic questions may involve patterns of spatial variability only and tactical questions may involve temporal as well as spatial variability. This difference may have several practical consequences. In the strategic use of remote-sensing data, extreme speed in the delivery of the image or image data after acquisition may be unnecessary. Speed may be necessary for tactical management uses. Using remote sensing in strategic situations may not require calibrated image data and may not require as many images. If true, this could result in considerably lower data-collection costs. The objective of this research was to seek answers to two questions: (i) can uncalibrated data be used for strategic management ? (ii) what is the inter-temporal relationship among sequences of images ? Analyses of chronological sequences of images of irrigation and N stress indicate that uncalibrated data are useful for addressing strategic questions that involve spatial variability of crop status and that locations within the field are highly autocorrelated, so that relatively few images are necessary to determine crop spatial reflectance properties.

[19]
Imhoff M L, Tucker C J, Lawrence W T, et al.The use of multisource satellite and geospatial data to study the effect of urbanization on primary productivity in the United States[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000,38:2549-2556.Data from two different satellites, a digital land cover map, and digital census data were analyzed and combined in a geographic information system to study the effect of urbanization on photosynthetic productivity in the United States. Results show that urbanization can have a measurable but variable impact on the primary productivity of the land surface. Annual productivity can be reduced by as much as 20 days in some areas, but in resource limited regions, photosynthetic production can be enhanced by human activity. Overall, urban development reduces the productivity of the land surface, and those areas with the highest productivity are directly in the path of urban sprawl.

DOI

[20]
Milesi C,Running S W.Global Vegetation Production and Population Distribution[C]. Boston: American Geophysical Union, 2001:B51A-18.

[21]
Ma T, Zhou C H, Pei T, et al.Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities[J]. Remote Sensing of Environment, 2012,124:99-107.78 Night light could be an explanatory indicator for estimating urbanization dynamics. 78 Night lights show diverse responses to urbanization dynamics over China's cities. 78 Quantitative models for using night lights to estimate urbanization should vary.

DOI

[22]
何春阳,史培军,李景刚,等.基于DMSP/OLS夜间灯光数据和统计数据的中国大陆20世纪90年代城市化空间过程重建研究[J].科学通报,2006,51(7):856-861.中国大陆目前以行政单元为基础的城镇用地面积统计数据缺乏足够的空间信息, 难以满足大尺度城市化空间格局和变化过程研究的需要. 文中首先提出了一种以现有统计数据为基础, 借助DMSP/OLS夜间灯光遥感数据, 快速恢复和提取中国大陆城镇用地空间信息, 弥补现有统计资料不足的新方法. 进而以该方法为基础, 利用 1992, 1996和1998年的3期DMSP/OLS夜间灯光数据, 重建了中国大陆20世纪90年代的城市化空间过程. 利用统计数据对该方法提取结果的面积总量评估表明, 二者在全中国大陆尺度上的面积相对误差在1992年小于2%, 1996年和1998年则均小于1%, 在省级尺度, 相对误差最大的省份也没有超过10%, 大部分省份的相对误差小于3%. 同时利用高分辨率Landsat TM数据对该方法提取结果的城市空间格局特征分析也表明, 利用DMSP/OLS 提取的城市格局特征与Landsat TM提取的城市格局特征基本上是吻合的, 两者的相似程度在80%左右. 这说明利用该方法重建的中国大陆20世纪90年代城市空间过程, 基本上可以反映当时中国大陆城市发展的实际状况, 具有一定的可信性, 可以在一定程度上为中国大陆宏观城市空间格局和变化过程研究提供帮助.

DOI

[ He C Y, Shi P J, Li J G, et al.Reconstruction of urbanization process in mainland China in the 1990s based on DMSP/OLS night light data and statistical data[J]. Chinese Science Bulletin. 2006,51(7):856-861. ]

[23]
方精云,朴世龙,贺金生,等.近20年来中国植被活动在增强[J].中国科学(C辑:生命科学),2003,33(6):554-565. 为阐明近20年来中国植被覆盖变化的整体状况,利用归一化植被指数(NDVI)作为植被活动的指标,使用第3代NOAA-AVHRR/NDVI 时间序列数据,研究了1982～1999年间中国地区NDVI的变化.为消除地表非植被因素的影响,参考国际惯例,定义年NDVI≥0.1的地区为有植被覆盖地区(简称植被地区),NDVI0.1的地区为植被稀少地区.结果表明,18年来,我国大多数地区的NDVI都呈现不同程度的增加趋势,表明我国的植被活动在增强.与80年代初相比,90年代末植被地区的面积增加3.5％,植被稀少地区的面积下降了18.1％.全国平均年NDVI增加了7.4％.生长季节的延长和生长加速是我国NDVI增加的主要原因,而温度上升和夏季降水量的增加以及农业活动的加强可能是其主要的驱动因子.我国NDVI变化趋势显示了较大的空间异质性:东部沿海地区呈下降趋势或变化不明显;农业产区和西部地区增加显著.这种空间异质性是由于城市化过程、农业生产活动、区域气候特征以及植被对气候变化的区域响应等综合因素作用的结果.

DOI

[ Fang J Y, Piao S L He J S, et al. Vegetation activities have been increasing in China in recent 20 years[J]. Sciencein China SericesC: life Sciences, 2003,33(6):554-565. ]

[24]
李广宇,陈爽,张慧,等. 2000-2010年长三角地区植被生物量及其空间分布特征[J].生态与农村环境学报,2016,32(5):708-715.

[ Li G Y, Chen S, Zhang H, et al.Variation of spatial estimation and distribution of vegetation biomass in Yangtze River Delta during 2000-2010[J]. Journal of Ecology and Rural Environment, 2016,32(5):708-715. ]

[25]
周坚华,胡永红,周一凡,等.城镇绿地植被固碳量遥感测算模型的设计[J].生态学报,2010,30(20):5653-5665.城镇绿地是增加城市碳汇的积极因素之一。但城镇绿地植被结构和分布的极高空间异质性、影响植物生产力的环境压力因子的极高空间异质性等，使城镇绿地碳汇水平估算存在很大的不确定性。为此，提出了完全由遥感数据驱动的城镇绿地植被固碳量测算模型。它以分树种单株测算来适应城镇绿地植被结构和分布等的高异质性；以小尺度提取影响植物生产力的主要环境压力和管养模式因子，来适应这些因子空间分布的高异质性。该模型可以用于自动测算城镇绿地植被地上干生物量和地上净第一生产力，它的提出对于评价城镇绿地植被的碳捕获能力和储量分布、碳汇水平、以及由此产生的对城镇地区碳循环和生态承载力等的定量影响具有重要意义。为了论证该技术框架中一些关键技术的可行性，进行了局部试验，并取得了一些进展。

[ Zhou J H, Hu Y H, Zhou Y F, et al. A design of carbon-sink model of urban landscape vegetation driven by remote sensing[J]. Acta Ecologica Sinica, 2010,30(20):5653-5665. ]

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